Resource Efficient Framework for Remote Sensing Visual Recognition

Abstract

In the rapidly evolving field of remote sensing (RS), the need for efficient and accurate scene classification is paramount. RS imagery comprising satellite and aerial imagery often faces challenges such as varying scales and diverse environmental conditions, which can significantly affect the discernibility of important features. To address these challenges, this article introduces a lightweight dual-branch network architecture that adequately handles scale variations and complex scene compositions. The first branch, progressive feature processing branch (PFPB), of the proposed framework is engineered to extract rich multiple-scale features through collaborative parallel stages and intrabranch and interbranch connectivity with optimized computational resources. The second branch, InXformer branch (IXB) enhances the system’s capability to assimilate global context and long-range dependencies essential for comprehensive scene analysis utilizing an involution-based transformer approach. Experimental validation in three challenging datasets sourced from diverse aerial platforms demonstrates the greater effectiveness of the proposed network. The proposed network achieves a weighted ${F}1$ of 97.15% in the AIDERSv2 dataset, surpassing other methods such as DecoupleNet by more than 2%, while maintaining high efficiency with 0.41M parameters, lower computational overhead with 0.96 GFLOPs, and a higher processing speed of 4616 frames/s (FPS). With regards to WHU-RS19 and UCM datasets, the devised network achieves 93.69% and 94.57% weighted- ${F}1$ score, respectively. These results underscore the ability of the proposed network to efficiently handle diverse scene compositions by delivering state-of-the-art performance.